ICEAP: An advanced fine-grained image captioning network with enhanced attribute predictor

Abstract

Fine-grained image captioning is a focal point in the vision-to-language task and has attracted considerable attention for generating accurate and contextually relevant image captions. Effective attribute prediction and their utilization play a crucial role in enhancing image captioning performance. Despite progress in prior attribute-related methods, they either focus on predicting attributes related to the input image or concentrate on predicting linguistic context-related attributes at each time step in the language model. However, these approaches often overlook the importance of balancing visual and linguistic contexts, leading to ineffective exploitation of semantic information and a subsequent decline in performance. To address these issues, an Independent Attribute Predictor (IAP) is introduced to precisely predict attributes related to the input image by leveraging relationships between visual objects and attribute embeddings. Following this, an Enhanced Attribute Predictor (EAP) is proposed, initially predicting linguistic context-related attributes and then using prior probabilities from the IAP module to rebalance image and linguistic context-related attributes, thereby generating more robust and enhanced attribute probabilities. These refined attributes are then integrated into the language LSTM layer to ensure accurate word prediction at each time step. The integration of the IAP and EAP modules in our proposed image captioning with the enhanced attribute predictor (ICEAP) model effectively incorporates high-level semantic details, enhancing overall model performance. The ICEAP outperforms contemporary models, yielding significant average improvements of 10.62% in CIDEr-D scores for MS-COCO, 9.63% for Flickr30K and 7.74% for Flickr8K datasets using cross-entropy optimization, with qualitative analysis confirming its ability to generate fine-grained captions.